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- Title
A RSBU-LSTM network for radio frequency fingerprint identification relying on multiple features.
- Authors
Ling, Haoran; Zhu, Fengchao; Yao, Minli
- Abstract
Radio frequency fingerprint identification (RFFI) can distinguish highly similar wireless communication devices to protect physical layer security and improve the security of wireless networks effectively, which has been widely used for spectrum management and physical layer secure communication. However, most RFFI methods show a degradation of performance under low signal-to-noise ratio (SNR) environments. In this paper, we propose a RSBU-LSTM network relying on multiple features to improve the identification accuracy with low SNR. Firstly, we use multiple features of in-phase (I), quadrature (Q), and phase as inputs. Then, we use multiple Residual Shrinkage Building Units (RSBUs) to extract the correlation features within the cycle of signals and preserve as many features as possible in low SNR environments. Finally, we use the long short-term memory (LSTM) to extract the relevant features of the signals of non-adjacent cycles. The experimental results show that the proposed network can effectively complete RFFI in low SNR environments and show better performance than other models used for comparison.
- Subjects
RADIO frequency identification systems; PHYSICAL layer security; RADIO networks; WIRELESS communications security; SPECTRUM allocation; WIRELESS communications
- Publication
EURASIP Journal on Advances in Signal Processing, 2024, Vol 2024, Issue 1, p1
- ISSN
1687-6172
- Publication type
Article
- DOI
10.1186/s13634-024-01169-5